Fraud detection using multi-task learning and/or deep learning

ABSTRACT

Application of multi-task learning technique(s) to machine logic (for example, software) used to detect financial transactions that are fraudulent or at least considered likely to be fraudulent. Some embodiments include adjustments and/or additions to conventional multi-task learning techniques in order to make the multi-task learning techniques more suitable for use in fraud detection software. One example of this is compensation for class imbalances that are to be expected as between the likely-fraud and not-likely-fraud classes of data sets (for example, training data sets, runtime data sets).

BACKGROUND

The present invention relates generally to the fields of deep learningand machine logic used to detect fraudulent financial transactions (or,probably-fraudulent transactions, or possibly-fraudulenttransactions—typically the “detection” of fraud does not have a onehundred percent accuracy rate, but rather flags certain series oftransactions for further investigation and/or clears transactions asbeing deemed unlikely to be part of a fraud).

The finance industry oversees billions of transactions between consumersand merchants and fraud is increasingly becoming a daunting problem.Several major corporations use computer implemented fraud detectionalgorithms to assist with the objective of detecting fraudulentfinancial transactions. Typically these algorithms are rule basedapproaches which are painstakingly engineered by observing the frauds.These approaches are vulnerable because a fraudulent actor may becomeaware of the fraud detection rules, and then structure her fraudulentactivities to avoid detection by machine logic-based rules. Industry hasalso started using machine learning (ML) based approaches for predictionwhich typically require extensive feature engineering.

The Wikipedia entry for “deep learning” (as of 1 May 2020) states, inpart, as follows: “Deep learning (also known as deep structuredlearning) is part of a broader family of machine learning methods basedon artificial neural networks with representation learning. Learning canbe supervised, semi-supervised or unsupervised. Deep learningarchitectures such as deep neural networks, deep belief networks,recurrent neural networks and convolutional neural networks have beenapplied to fields . . . Artificial neural networks (ANNs) were inspiredby information processing and distributed communication nodes inbiological systems. ANNs have various differences from biologicalbrains. Specifically, neural networks tend to be static and symbolic,while the biological brain of most living organisms is dynamic (plastic)and analog. . . . Deep learning is a class of machine learningalgorithms that . . . uses multiple layers to progressively extracthigher level features from the raw input. For example, in imageprocessing, lower layers may identify edges, while higher layers mayidentify the concepts relevant to a human such as digits or letters orfaces. . . . Most modern deep learning models are based on artificialneural networks, specifically, Convolutional Neural Networks (CNN)s,although they can also include propositional formulas or latentvariables organized layer-wise in deep generative models such as thenodes in deep belief networks and deep Boltzmann machines. In deeplearning, each level learns to transform its input data into a slightlymore abstract and composite representation. . . . Importantly, a deeplearning process can learn which features to optimally place in whichlevel on its own. (Of course, this does not completely eliminate theneed for hand-tuning; for example, varying numbers of layers and layersizes can provide different degrees of abstraction.) The word ‘deep’ in‘deep learning’ refers to the number of layers through which the data istransformed. More precisely, deep learning systems have a substantialcredit assignment path (CAP) depth. The CAP is the chain oftransformations from input to output. CAPs describe potentially causalconnections between input and output. For a feedforward neural network,the depth of the CAPs is that of the network and is the number of hiddenlayers plus one (as the output layer is also parameterized). Forrecurrent neural networks, in which a signal may propagate through alayer more than once, the CAP depth is potentially unlimited. Nouniversally agreed upon threshold of depth divides shallow learning fromdeep learning, but most researchers agree that deep learning involvesCAP depth higher than 2. CAP of depth 2 has been shown to be a universalapproximator in the sense that it can emulate any function. Beyond that,more layers do not add to the function approximator ability of thenetwork. Deep models (CAP>2) are able to extract better features thanshallow models and hence, extra layers help in learning the featureseffectively.” (footnotes omitted)

The Wikipedia entry for “multi-task learning” (as of 6 May 2020) states,in part, as follows: “Multi-task learning (MTL) is a subfield of machinelearning in which multiple learning tasks are solved at the same time,while exploiting commonalities and differences across tasks. This canresult in improved learning efficiency and prediction accuracy for thetask-specific models, when compared to training the models separately.Early versions of MTL were called ‘hints.’ In a widely cited 1997 paper,Rich Caruana gave the following characterization: ‘Multitask Learning isan approach to inductive transfer that improves generalization by usingthe domain information contained in the training signals of relatedtasks as an inductive bias. It does this by learning tasks in parallelwhile using a shared representation; what is learned for each task canhelp other tasks be learned better.’ In the classification context, MTLaims to improve the performance of multiple classification tasks bylearning them jointly. One example is a spam-filter, which can betreated as distinct but related classification tasks across differentusers. To make this more concrete, consider that different people havedifferent distributions of features which distinguish spam emails fromlegitimate ones, for example an English speaker may find that all emailsin Russian are spam, not so for Russian speakers. Yet there is adefinite commonality in this classification task across users, forexample one common feature might be text related to money transfer.Solving each user's spam classification problem jointly via MTL can letthe solutions inform each other and improve performance. Furtherexamples of settings for MTL include multiclass classification andmulti-label classification. Multi-task learning works becauseregularization induced by requiring an algorithm to perform well on arelated task can be superior to regularization that prevents overfittingby penalizing all complexity uniformly. One situation where MTL may beparticularly helpful is if the tasks share significant commonalities andare generally slightly under sampled. However, as discussed below, MTLhas also been shown to be beneficial for learning unrelated tasks.”(footnotes omitted)

It is further noted that multi-task learning is understood by those ofskill in the art to be a higher level concept where the structureunderneath the multi-task learning implementation code is some varietyof a machine learning structure (that is, some variety of a machinelearning algorithm). As used herein, the term “multi-task learningalgorithm” will be used to collectively refer to both of the underlyinglearning structure and the multi-task instructions.

SUMMARY

According to an aspect of the present invention, there is a method,computer program product and/or system that performs the followingoperations (not necessarily in the following order): (i) training amulti-task learning algorithm including an underlying learning structureand multi-task instructions, the multi-task learning algorithm beingprogrammed and/or structured to receive new data sets representingfinancial transactions and to selectively provide likely-frauddeterminations and likely-not-fraud determinations; (ii) receiving, bythe multi-task learning algorithm, a first new data set representing afirst financial transaction; and (iii) applying the multi-task learningalgorithm to data of the first new data set to determine that the firstfinancial transaction is likely-fraud. The multi-task learninginstructions: (a) solve multiple learning tasks in a temporallyoverlapping manner while exploiting commonalities and differences acrosstasks resulting in improved learning efficiency and prediction accuracyfor the task-specific models, when compared to training multiple modelsseparately, and (b) improve generalization by using the domaininformation contained in the training signals of related tasks as aninductive bias by learning tasks in parallel while using a sharedrepresentation so that what is learned for each task can help othertasks be learned.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a first embodiment of a system according tothe present invention;

FIG. 2 is a flowchart showing a first embodiment method performed, atleast in part, by the first embodiment system;

FIG. 3 is a block diagram showing a machine logic (for example,software) portion of the first embodiment system;

FIG. 4 is a screenshot view generated by the first embodiment system;and

FIG. 5 is a block diagram of a second embodiment of a system accordingto the present invention.

DETAILED DESCRIPTION

Some embodiments of the present invention are directed to theapplication of multi-task learning technique(s) to machine logic (forexample, software) used to detect financial transactions that arefraudulent or at least considered likely to be fraudulent. Someembodiments include adjustments and/or additions to conventionalmulti-task learning techniques in order to make the multi-task learningtechniques more suitable for use in fraud detection software. Oneexample of this is compensation for class imbalances that are to beexpected as between the likely-fraud and not-likely-fraud classes ofdata sets (for example, training data sets, runtime data sets). ThisDetailed Description section is divided into the following subsections:(i) The Hardware and Software Environment; (ii) Example Embodiment;(iii) Further Comments and/or Embodiments; and (iv) Definitions.

I. THE HARDWARE AND SOFTWARE ENVIRONMENT

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (for example, lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

A “storage device” is hereby defined to be anything made or adapted tostore computer code in a manner so that the computer code can beaccessed by a computer processor. A storage device typically includes astorage medium, which is the material in, or on, which the data of thecomputer code is stored. A single “storage device” may have: (i)multiple discrete portions that are spaced apart, or distributed (forexample, a set of six solid state storage devices respectively locatedin six laptop computers that collectively store a single computerprogram); and/or (ii) may use multiple storage media (for example, a setof computer code that is partially stored in as magnetic domains in acomputer's non-volatile storage and partially stored in a set ofsemiconductor switches in the computer's volatile memory). The term“storage medium” should be construed to cover situations where multipledifferent types of storage media are used.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As shown in FIG. 1, networked computers system 100 is an embodiment of ahardware and software environment for use with various embodiments ofthe present invention. Networked computers system 100 includes: serversubsystem 102 (sometimes herein referred to, more simply, as subsystem102); client subsystems 104, 106, 108, 110, 112; and communicationnetwork 114. Server subsystem 102 includes: server computer 200;communication unit 202; processor set 204; input/output (I/O) interfaceset 206; memory 208; persistent storage 210; display 212; externaldevice(s) 214; random access memory (RAM) 230; cache 232; and program300.

Subsystem 102 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, or any other type of computer (seedefinition of “computer” in Definitions section, below). Program 300 isa collection of machine readable instructions and/or data that is usedto create, manage and control certain software functions that will bediscussed in detail, below, in the Example Embodiment subsection of thisDetailed Description section.

Subsystem 102 is capable of communicating with other computer subsystemsvia communication network 114. Network 114 can be, for example, a localarea network (LAN), a wide area network (WAN) such as the internet, or acombination of the two, and can include wired, wireless, or fiber opticconnections. In general, network 114 can be any combination ofconnections and protocols that will support communications betweenserver and client subsystems.

Subsystem 102 is shown as a block diagram with many double arrows. Thesedouble arrows (no separate reference numerals) represent acommunications fabric, which provides communications between variouscomponents of subsystem 102. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a computer system. Forexample, the communications fabric can be implemented, at least in part,with one or more buses.

Memory 208 and persistent storage 210 are computer-readable storagemedia. In general, memory 208 can include any suitable volatile ornon-volatile computer-readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 214 may be able tosupply, some or all, memory for subsystem 102; and/or (ii) devicesexternal to subsystem 102 may be able to provide memory for subsystem102. Both memory 208 and persistent storage 210: (i) store data in amanner that is less transient than a signal in transit; and (ii) storedata on a tangible medium (such as magnetic or optical domains). In thisembodiment, memory 208 is volatile storage, while persistent storage 210provides nonvolatile storage. The media used by persistent storage 210may also be removable. For example, a removable hard drive may be usedfor persistent storage 210. Other examples include optical and magneticdisks, thumb drives, and smart cards that are inserted into a drive fortransfer onto another computer-readable storage medium that is also partof persistent storage 210.

Communications unit 202 provides for communications with other dataprocessing systems or devices external to subsystem 102. In theseexamples, communications unit 202 includes one or more network interfacecards. Communications unit 202 may provide communications through theuse of either or both physical and wireless communications links. Anysoftware modules discussed herein may be downloaded to a persistentstorage device (such as persistent storage 210) through a communicationsunit (such as communications unit 202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication with servercomputer 200. For example, I/O interface set 206 provides a connectionto external device set 214. External device set 214 will typicallyinclude devices such as a keyboard, keypad, a touch screen, and/or someother suitable input device. External device set 214 can also includeportable computer-readable storage media such as, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention, forexample, program 300, can be stored on such portable computer-readablestorage media. I/O interface set 206 also connects in data communicationwith display 212. Display 212 is a display device that provides amechanism to display data to a user and may be, for example, a computermonitor or a smart phone display screen.

In this embodiment, program 300 is stored in persistent storage 210 foraccess and/or execution by one or more computer processors of processorset 204, usually through one or more memories of memory 208. It will beunderstood by those of skill in the art that program 300 may be storedin a more highly distributed manner during its run time and/or when itis not running. Program 300 may include both machine readable andperformable instructions and/or substantive data (that is, the type ofdata stored in a database). In this particular embodiment, persistentstorage 210 includes a magnetic hard disk drive. To name some possiblevariations, persistent storage 210 may include a solid state hard drive,a semiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer-readable storage media that is capable of storing programinstructions or digital information.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

II. EXAMPLE EMBODIMENT

As shown in FIG. 1, networked computers system 100 is an environment inwhich an example method according to the present invention can beperformed. As shown in FIG. 2, flowchart 250 shows an example methodaccording to the present invention. As shown in FIG. 3, program 300performs or controls performance of at least some of the methodoperations of flowchart 250. This method and associated software willnow be discussed, over the course of the following paragraphs, withextensive reference to the blocks of FIGS. 1, 2 and 3.

Processing begins at operation S260, where train multi-task learningmodule (“mod”) 306 trains a multi-task learning algorithm 308 usinghistorical data received from client sub-system 104 throughcommunication network 114 (see FIG. 1). In this example, the trainingtakes the form of both supervised and unsupervised learning. Thehistorical data used for training is data related to previous real worldfinancial transactions, some of which involved fraud and some of whichdid not involve fraud. In this way multi-task learning algorithm 308 istrained to detect likely, or potential, fraud. This multi-task learningalgorithm meets the definition of multi-task learning algorithms setforth, above, in the Background section.

As shown in FIG. 3, multi-task learning algorithm 308 includes:underlying learning structure 302; and multi-task instructions 304.Underlying learning structure 302 is, in this example embodiment, a deeplearning style learning structure that performs deep learning (see thedefinition of “deep learning,” above, in the Background section).Alternatively, other types of machine learning structures (now known orto be developed in the future) may be used as the underlying learningstructure. The multi-task instructions cause the underlying learningstructure to be accessed (for example, trained, re-trained and/or usedto do fraud detection procedures on new financial transactions) in amulti-task manner. The multi-tasking aspects of the present inventionare discussed in more detail, below, in the next sub-section of thisDetailed Description.

Processing proceeds to operation S265, where receive input data mod 310receives data relating to a new financial transaction involving a bank(represented by client sub-system 106), a customer (represented byclient sub-system 108) and a credit card company (represented by clientsub-system 110). Program 300 will determine whether the currentfinancial transaction raises concerns about potential fraud by thecustomer.

Processing proceeds to operation S275, where fraud determination mod 312applies underlying learning structure 302, running on encoder neuralnetwork software 314, to the input data characterizing the currentfinancial transaction to determine whether fraud is likely. In thisexample, fraud likelihood quotient is determined to be 9.9 out of 10.0.Encoder neural network software 314, in this embodiment, is anencoder-decoder (although, as is the practice of the art, onencoder-decoder may be referred to more simply as an “encoder”). Thesub-system that includes multitask learning algorithm and encoder neuralnetwork upon which the multitask is performed as herein be referred toas an “ensemble classifier component,” or, more simply, as an “ensembleclassifier.”

Processing proceeds to operation S280, where fraud determination moduledetermines that fraud is sufficiently likely such that furtherinvestigation should take place. More specifically, in this example, ifthe fraud likelihood quotient, of 9.9, is greater than a threshold valueof 9.0, meaning that corrective action should be taken. Processingproceeds to operation S285 where take corrective action mod 316 takescorrective action in response to the likely fraud (as shown by the fraudalert text message screen shot 400 of FIG. 4). In this example, thetypes of possible corrective actions are as follows: (i) decline thetransaction so the purchase is not completed; (ii) annotate this accountas having suspicious activity so as to increase the likelihood that afuture transaction is labeled as fraud; (iii) invalidate this cardnumber so it cannot be used in future transactions; (iv) increment thenumber of suspicious purchases at this merchant so that future purchasesat the merchant will be more likely to be labelled fraud; and (v) usethis example to improve training and accuracy of fraud detection models.In some embodiments, a text message is corrective action in the form oftwo-factor authentication, where a separate, non-compromised form ofcommunication is used to check the integrity of another (the cardtransaction).

In the embodiment of FIGS. 2 and 3, a multitask learning algorithm,including an underlying learning structure (for example, a deep learningstructure) and multi-task instructions, is applied to the field of frauddetection (for example, fraud detection in financial transactionsinvolving transacting entities and money). The model of the example ofFIGS. 2 and 3 use a single learning structure (that is underlyinglearning structure 102, which, in this example, happens to be a deeplearning structure). Alternatively, underlying learning structures otherthan deep learning type learning structures may be used as theunderlying learning structure (which is nevertheless controlled in amultitask manner).

As a further alternative, some embodiments of the present invention mayuse an “ensemble model” meaning that more than one learning structure isused (for example, a deep learning structure and a non-deep-learningtype learning structure. In an ensemble model embodiment, the model ofthe multiple learning structures are combined to obtain predictionsbetter than any of the individual models yields individually. One, someor all of the underlying learning structures in an ensemble modelembodiment may be controlled in a multitask manner. In some ensemblemodel embodiments, a new financial transaction is determined to belikely-fraud only if all of the multiple underlying learning structuresdeclare fraud. In other ensemble model embodiments, a new financialtransaction is determined to be likely-fraud if a weighted combinationof the results obtained from the multiple underlying learning structuresso indicates. Other functions can be used to combine the output ofmultiple underlying learning structures in ensemble model embodiments ofthe present invention (for example, likely-fraud is considered to bedetermined only if a majority of the multiple underlying learningstructures so indicate).

One feature that may be used to help apply multi-task learningalgorithms specifically to the field of fraud detection is imbalancecompensation instructions to compensate for the fact that fraud-likelyand fraud-not-likely classes of data sets are typically highlyimbalanced, with a great prevalence of fraud-not-likely data sets.

III. FURTHER COMMENTS AND/OR EMBODIMENTS

Some embodiments of the present invention recognize one, or more, of thefollowing facts, potential problems and/or potential areas forimprovement with respect to the current state of the art: (i) from ascientific point of view, the impact of multi-task based regularizationis studied in highly imbalanced fraud detection problems; and/or (ii)from a business point of view, a good fraud detection approachtranslates directly into saving millions of dollars that would otherwisebe lost to fraud.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) a multi-task learning based deep learning approach for credit frauddetection; (ii) detects fraudulent transactions using machine logic (forexample, software); (iii) a method that uses an imbalance sensitive lossfunction along with an auxiliary task component to learn an effectivemodel for fraud detection; and/or (iv) the model, through its objectiveis able to handle differences in the minority class from diverse tospecific patterns, which is potentially useful when it is not known howto balance focus on the minority class versus the majority class; and/or(v) an effective ensemble with tree based models.

Some embodiments of the present invention use a model that is robust todiverse fraud scenarios, thereby providing an effective solution. Fraudpatterns can vary in different circumstances. A model which canconveniently fit the given circumstance is highly valuable. In someembodiments, inference time on CPU (central processing unit) is similarto popular ensemble models used in industry. Relative simplicity of themodel allows for fast inference satisfying real life timingrequirements. Some embodiments avoid feature engineering by using a deeplearning approach. Some embodiments obtain results that demonstrate asignificant reduction in false positives while keeping false negativesnearly unaffected. Considerable value is thereby added by reducingconsumer discomfort. Some embodiments can work with other fraud filtersto effectively allow for better decision making and deciding the urgencylevel given the situation.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) a robust solution focusing on the problem with the of frauddetection that: (a) entails relatively few false negatives (missedfrauds), (b) relatively few false positives will which can lead to(annoyed customers incorrectly flagged for further fraud investigation),and (c) is adaptive to changing situations; (ii) unlike images or text,transaction related data has heterogenous features that are a mixture offeatures that are a mixture of continuous (for example, $0-$10,000) andcategorical attributes (for example, 1=Bob, 2=Jane, 3=Sally, etc. mapsnames to numbers); (iii) these categorical features are representedusing embedding because the categorical numbering is arbitrary andconfusing for models; (iv) uses multi-task regularization in the settingof extreme class imbalance (a very small fraction of credit cardtransactions are fraudulent) to add robustness, i.e. not learn erroneousfacts (e.g. that all transactions made at 3 minutes past the hour arefraudulent even though that may have been the case in the trainingdata)—and further noting per Wikipedia that (a) in multi-task learning,multiple learning tasks are solved at the same time, while exploitingcommonalities and differences across tasks; and (b) regularization isthe process of adding information in order to solve an ill-posed problemor to prevent overfitting; (v) allows for several meaningfulalternatives for the auxiliary task (for example, reconstruction fromthe fraud model of the transaction details and prediction of user ID ofthe person making a purchase); (vi) employs an instance-sensitive lossalso known as “error”) function which, as part of multi-task lossfunction, allows the model to handle different distributions of frauds,which is diverse across more specific patterns of fraud); (vii) uses amodel that is modular in nature, thereby allowing for other deeplearning classifiers to be easily used in the model with similarbenefits; and/or (viii) uses an ensemble model that combines multipleindividual (fraud detection) models to create a better prediction thanany of the individual models (for example, using weighted averaging tocombine predictions from a tree based model and a deep learning models).

Further with respect to item (iii) in the previous paragraph, embeddinghelps counter the arbitrariness and confusion of assigning an orderedseries (for example, integer numbers) to a set of objects (for example,people) that has no inherent ordering. For example, 2=Jane is not reallycloser to 1=Bob than 3=Sally. An embedding makes numbers comparable. Forexample the embedding known as one-hot encoding creates a binary featurefor each name: 1 if the name is present and 0 otherwise, so a numericsyntax might have 1-0-0=Bob, 0-1-0=Jane, and 0-0-1=Sally.

More specifically in FIG. 5, diagram 500 includes: embedding block 502(this block generates an embedding of each categorical feature in thetransaction, such as but not limited to merchant name, zip code, etc.,as a vector representation, typically in low-dimension, to efficientlycapture and encode the feature relationships in the transaction data);continuous features block 504 (this block generates a scaled and/ornormalized feature for the continuous values, such as but not limited tothe amount and time of transaction); encoder neural network 506 (thisblock generates a distilled representation in latent space capturing thecomplex interactions of the categorical and continuous features in theoriginal transaction data=the encoder neural network block 506 is notlimited to feed-forward networks and could be sequential); auxiliarynetwork 508 (this block takes the intermediate encoded representation toperform an auxiliary task, which in one embodiment is to reconstruct(generate) some input feature, such as but not limited to the user id orage, in the transaction using the same latent features as theclassifier); classification network block 510 (this block takes in theencoder's representation to classify (predict) the transaction asfraudulent (or not fraudulent)); and gradient boosting framework 512.Gradient boosting framework 512 is combined with the classificationnetwork 510 to obtain the prediction(s) as fraudulent (ornon-fraudulent) transactions. Auxiliary network 508 is used only duringthe training phase and not during the inference phase.

Embedding block 502 and continuous features block 504 represent two (2)types of input data received pursuant to a credit card transaction.Encoder neural network block 506 converts the inputs to an internalrepresentation more amenable to analysis performed at blocks downstreamof block 506. Auxiliary network 508 performs auxiliary tasks likepredicting the identity of the card holder based on other information inthe transaction. This is a capability which can be used to furtherimprove fraud detection. Classification network block 510 classifiesfraud/non-fraud. In various embodiments of the present invention,classification network block 510 take many forms such as, neuralnetwork-based approach, a gradient boosted approach, logisticregression, any other classification algorithm and/or variouscombinations of the foregoing enumerate types.

An objective function is a mathematical statement that reflects thevalue of a quantity to be optimized. For example, if one wants to seehow high a ball will be launched in the air under the influence of agiven force, the objective function might give height for a given angleat which the ball is thrown. The objective function for an embodiment ofa deep learning model, applicable to various embodiments of the presentinvention, is given by Expression (1):

−α(1−p_t){circumflex over ( )}γ log(p_t)+|x_input−x_decoder|  (1)

Where: (i) x_input: Input generated from embedding block 502 in FIG. 5;(ii) x_decoder: Decoded output from a decoder, as one embodiment of theauxiliary network 508 in FIG. 5; (iii) p_t: Predicted class probability(i.e. model's assessment of the probability that the transaction is inthe fraudulent or non-fraudulent class of transactions), as the outputof classification network block 510 in FIG. 5; (iv) γ: Exponent forweighting the loss term to focus on the hard-to-classify cases(fraudulent transactions); and (v) α: Class weight assigned tofraudulent (and non-fraudulent) examples to offset the imbalance in thedataset. The objective (or loss or error) function in Expression (1)includes two components, (a) the error from the classifier network (510)and (b) the error from the auxiliary network (508). Different errorfunctions could be employed for (a) and (b), for example, classifiernetwork block 510 could use “focal loss,” which gives less weight tovalues in the common case (no fraud), and auxiliary network 508 coulduse another error function, for example mean square error. Given thedatasets for fraud-detection are highly imbalanced, often with less than0.1% of all the transactions being labelled as fraudulent, the totalloss contribution from easy-to-classify (non-fraudulent) examplessignificantly outweigh the hard-to-classify examples. Hence, the firstterm in the objective function is introduced with γ, usually set to >0,to force focus on the hard-to-classify fraudulent cases. The focal lossapproach has been used in other domains, such as object detection, andit is a preferred embodiment for this invention for handling highlyimbalanced data of the sort seen with fraud-detection model training.The second term, in one embodiment, is the reconstruction loss from theauxiliary task (network), which captures the loss in the network'sability to reconstruct some input feature faithfully, thereby forcingthe end-to-end network to learn rich latent features that helps toimprove the classifier performance (accuracy, reduced false-positivesand false-negatives). A different auxiliary task can be used by changingthe second term of the objective appropriately.

A method of utilizing identifying a likelihood of credit card fraud,according to an embodiment of the present invention, includes thefollowing operations (not necessarily in the following order): (i)receiving by a computing device a credit card transaction for alikelihood of credit card fraud assessment, the credit card transactionattempted by a consumer; (ii) receiving by the computing deviceconsumer-specific credit risk information, the user-specific credit riskinformation specific to the consumer; (iii) receiving by the computingdevice global credit risk information; (iv) encoding by the computingdevice a neural network based upon the consumer-specific credit riskinformation and the global credit risk information; and (v) utilizing bythe computing device the neural network to assess the likelihood ofcredit card fraud for the credit card transaction. In this embodiment ofa method, encoding of the neural network includes computation of anerror function to address class imbalance.

More specifically, in one example of imbalance compensation instructionsaccording to an embodiment of the present invention, there are twoclasses: fraudulent transactions and non-fraudulent transactions. Thesetwo classes are imbalanced, relative to each other, due to the fact thatnon-fraudulent transactions heavily outnumber fraudulent (roughly 1000:1in this example). In computing the error during training of the model,the contribution to the error value will be dominated by themispredicted non-fraudulent transactions (that is, false positiveassessments of fraud). Therefore, some embodiments of the presentinvention use focal loss as the error function because use of focal losshelps to offset this imbalance by giving higher weight to mispredictedfraudulent transactions.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) receiving transaction information by user along with user/cardspecific details with labels for fraud/non fraud; (ii) all the userrecords are utilized together with their labels as training data toprovide global picture; (iii) in utilization through a multi-taskframework where one task predicts fraud likelihood whereas the otherauxiliary task focuses on user specific traits; (iv) performs multi-tasklearning with focus solely on the task of honoring/dishonoring thecheck; (v) solves a class imbalance problem, where a credit card fraud'sextreme imbalance (1/500 to 1/1000) may be present in the machinelearning workloads; (vi) focuses on credit cards (feature A) and onlinetransactions using a pattern of purchases for credit cards; (vii) uses asupervised approach and has an end-to-end training pipeline utilizingMulti-task Learning, which enables better discrimination of fraud in abroad range of conditions; (viii) use of multi-task learning in ourfiling enables better discrimination of fraud in a broad range ofconditions; (ix) uses an approach that is deep learning based andmulti-task based; (x) uses a fully automated approach; (xi) uses theinput transaction information from all users with the aid of amulti-task approach where an auxiliary task apart from fraud detectionis focused on user specific factors (for example, the auxiliary task maybe performed by an autoencoder that simply tries to reproduce somefeatures of the input); (xii) uses neural networks on a datasetconstituted by all the information from transactions of all users(herein sometimes referred to as global information); and/or (xiii) usesmulti-task learning with an auxiliary task to capture user specifictraits for example, predicting the identity of the user or the age ofthe user.

IV. DEFINITIONS

Present invention: should not be taken as an absolute indication thatthe subject matter described by the term “present invention” is coveredby either the claims as they are filed, or by the claims that mayeventually issue after patent prosecution; while the term “presentinvention” is used to help the reader to get a general feel for whichdisclosures herein are believed to potentially be new, thisunderstanding, as indicated by use of the term “present invention,” istentative and provisional and subject to change over the course ofpatent prosecution as relevant information is developed and as theclaims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautionsapply to the term “embodiment.”

and/or: inclusive or; for example, A, B “and/or” C means that at leastone of A or B or C is true and applicable.

Including/include/includes: unless otherwise explicitly noted, means“including but not necessarily limited to.”

Module/Sub-Module: any set of hardware, firmware and/or software thatoperatively works to do some kind of function, without regard to whetherthe module is: (i) in a single local proximity; (ii) distributed over awide area; (iii) in a single proximity within a larger piece of softwarecode; (iv) located within a single piece of software code; (v) locatedin a single storage device, memory or medium; (vi) mechanicallyconnected; (vii) electrically connected; and/or (viii) connected in datacommunication.

Computer: any device with significant data processing and/or machinereadable instruction reading capabilities including, but not limited to:desktop computers, mainframe computers, laptop computers,field-programmable gate array (FPGA) based devices, smart phones,personal digital assistants (PDAs), body-mounted or inserted computers,embedded device style computers, application-specific integrated circuit(ASIC) based devices.

What is claimed is:
 1. A computer-implemented method (CIM) comprising:training a multi-task learning algorithm including an underlyinglearning structure and multi-task instructions, the multi-task learningalgorithm being programmed and/or structured to receive new data setsrepresenting financial transactions and to selectively providelikely-fraud determinations and likely-not-fraud determinations;receiving, by the multi-task learning algorithm, a first new data setrepresenting a first financial transaction; and applying the multi-tasklearning algorithm to data of the first new data set to determine thatthe first financial transaction is likely-fraud; wherein the multi-tasklearning instructions: (i) solve multiple learning tasks in a temporallyoverlapping manner while exploiting commonalities and differences acrosstasks resulting in improved learning efficiency and prediction accuracyfor the task-specific models, when compared to training multiple modelsseparately, and (ii) improve generalization by using the domaininformation contained in the training signals of related tasks as aninductive bias by learning tasks in parallel while using a sharedrepresentation so that what is learned for each task can help othertasks be learned.
 2. The CIM of claim 1 further comprising: responsiveto the determination that the first financial transaction islikely-fraud, outputting a notification of possible fraud with respectto the first financial transaction.
 3. The CIM of claim 1 wherein theunderlying learning structure is a deep learning style learningstructure.
 4. The CIM of claim 1 where the first new data set includes:a first input parameter value corresponding to a first input parameter,with the first input parameter being a continuous type parameter; and asecond input parameter value corresponding to a second input parameter,with the second input parameter being a categorical type parameter. 5.The CIM of claim 1 wherein the application of the multi-task learningalgorithm is performed by an encoder-decoder neural network.
 6. The CIMof claim 1 wherein the multi-task instructions include imbalancecompensation instructions that compensate for imbalance betweenlikely-fraud data sets and likely-not-fraud data sets.
 7. The CIM ofclaim 6 further comprising: learning, under control of the imbalancecompensation instructions, to reproduce input features in a newfinancial transaction in a manner that compensates for the imbalancebetween likely-fraud data sets and likely-not-fraud data sets; andclassifying the new financial transaction as likely-fraud orlikely-not-fraud.
 8. A computer program product (CPP) comprising: a setof storage device(s); and computer code stored collectively in the setof storage device(s), with the computer code including data andinstructions to cause a processor(s) set to perform at least thefollowing operations: training a multi-task learning algorithm includingan underlying learning structure and multi-task instructions, themulti-task learning algorithm being programmed and/or structured toreceive new data sets representing financial transactions and toselectively provide likely-fraud determinations and likely-not-frauddeterminations, receiving, by the multi-task learning algorithm, a firstnew data set representing a first financial transaction, and applyingthe multi-task learning algorithm to data of the first new data set todetermine that the first financial transaction is likely-fraud; whereinthe multi-task learning instructions: (i) solve multiple learning tasksin a temporally overlapping manner while exploiting commonalities anddifferences across tasks resulting in improved learning efficiency andprediction accuracy for the task-specific models, when compared totraining multiple models separately, and (ii) improve generalization byusing the domain information contained in the training signals ofrelated tasks as an inductive bias by learning tasks in parallel whileusing a shared representation so that what is learned for each task canhelp other tasks be learned.
 9. The CPP of claim 8 wherein the computercode further includes data and instructions for causing the processor(s)set to perform the following operation(s): responsive to thedetermination that the first financial transaction is likely-fraud,outputting a notification of possible fraud with respect to the firstfinancial transaction.
 10. The CPP of claim 8 wherein the underlyinglearning structure is a deep learning style learning structure.
 11. TheCPP of claim 8 where the first new data set includes: a first inputparameter value corresponding to a first input parameter, with the firstinput parameter being a continuous type parameter; and a second inputparameter value corresponding to a second input parameter, with thesecond input parameter being a categorical type parameter.
 12. The CPPof claim 8 wherein the application of the multi-task learning algorithmis performed by an encoder-decoder neural network.
 13. The CPP of claim8 wherein the multi-task instructions include imbalance compensationinstructions that compensate for imbalance between likely-fraud datasets and likely-not-fraud data sets.
 14. The CPP of claim 13 wherein thecomputer code further includes data and instructions for causing theprocessor(s) set to perform the following operation(s): learning, undercontrol of the imbalance compensation instructions, to reproduce inputfeatures in a new financial transaction in a manner that compensates forthe imbalance between likely-fraud data sets and likely-not-fraud datasets; and classifying the new financial transaction as likely-fraud orlikely-not-fraud.
 15. A computer system (CS) comprising: a processor(s)set; a set of storage device(s); and computer code stored collectivelyin the set of storage device(s), with the computer code including dataand instructions to cause the processor(s) set to perform at least thefollowing operations: training a multi-task learning algorithm includingan underlying learning structure and multi-task instructions, themulti-task learning algorithm being programmed and/or structured toreceive new data sets representing financial transactions and toselectively provide likely-fraud determinations and likely-not-frauddeterminations, receiving, by the multi-task learning algorithm, a firstnew data set representing a first financial transaction, and applyingthe multi-task learning algorithm to data of the first new data set todetermine that the first financial transaction is likely-fraud; whereinthe multi-task learning instructions: (i) solve multiple learning tasksin a temporally overlapping manner while exploiting commonalities anddifferences across tasks resulting in improved learning efficiency andprediction accuracy for the task-specific models, when compared totraining multiple models separately, and (ii) improve generalization byusing the domain information contained in the training signals ofrelated tasks as an inductive bias by learning tasks in parallel whileusing a shared representation so that what is learned for each task canhelp other tasks be learned.
 16. The CS of claim 15 wherein the computercode further includes data and instructions for causing the processor(s)set to perform the following operation(s): responsive to thedetermination that the first financial transaction is likely-fraud,outputting a notification of possible fraud with respect to the firstfinancial transaction.
 17. The CS of claim 15 wherein the application ofthe multi-task learning algorithm is performed by an encoder-decoderneural network.
 18. The CS of claim 15 wherein the multi-taskinstructions include imbalance compensation instructions that compensatefor imbalance between likely-fraud data sets and likely-not-fraud datasets.
 19. The CS of claim 18 wherein the computer code further includesdata and instructions for causing the processor(s) set to perform thefollowing operation(s): learning, under control of the imbalancecompensation instructions, to reproduce input features in a newfinancial transaction in a manner that compensates for the imbalancebetween likely-fraud data sets and likely-not-fraud data sets; andclassifying the new financial transaction as likely-fraud orlikely-not-fraud.